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Learning to read scientific papers critically, which requires first grasping their main ideas and then raising critical thoughts, is important yet challenging for novice researchers. The traditional ways to develop critical paper reading (CPR) skills, e.g., checking general tutorials or taking reading courses, often can not provide individuals with adaptive and accessible support. In this paper, we first derive user requirements of a CPR training tool based on literature and a survey study (N=52). Then, we develop CriTrainer , an interactive tool for CPR training. It leverages text summarization techniques to train readers’ skills in grasping the paper’s main ideas. It further utilizes template-based generated questions to help them learn how to raise critical thoughts. A mixed-design study (N=24) shows that compared to a baseline tool with general CPR guidance, students trained by CriTrainer perform better in independently raising critical thinking questions on a new paper. We conclude with design considerations for CPR training tools.

Past research recognized that paper has many advantages over digital devices, such as affordability, tangibility, and flexibility. Paper, however, also lacks many of the functionalities available in digital technologies, such as access to online resources and the ability to display interactive content. Prior research therefore identified opportunities for fusing the two mediums into a combined interface. This work presents a literature review on this form of innovation - technologies that bridge the paper-digital gap. First, we synthesize an understanding of paper and its relationship with digital devices through the lens of past works. Then, we outline the state-of-the-art for paper-digital interfaces and highlight possible use cases and implementation approaches. Last, we discuss design considerations and future work for developing paper-digital interfaces. Our work may be beneficial for HCI researchers interested in the development of hybrid paper-digital interfaces, and more broadly in embedding digital functionalities in everyday objects. © 2021 ACM.

Past research recognized that paper has many advantages over digital devices, such as affordability, tangibility, and flexibility. Paper, however, also lacks many of the functionalities available in digital technologies, such as access to online resources and the ability to display interactive content. Prior research therefore identified opportunities for fusing the two mediums into a combined interface. This work presents a literature review on this form of innovation - technologies that bridge the paper-digital gap. First, we synthesize an understanding of paper and its relationship with digital devices through the lens of past works. Then, we outline the state-of-the-art for paper-digital interfaces and highlight possible use cases and implementation approaches. Last, we discuss design considerations and future work for developing paper-digital interfaces. Our work may be beneficial for HCI researchers interested in the development of hybrid paper-digital interfaces, and more broadly in embedding digital functionalities in everyday objects. © 2021 ACM.

Past research recognized that paper has many advantages over digital devices, such as affordability, tangibility, and flexibility. Paper, however, also lacks many of the functionalities available in digital technologies, such as access to online resources and the ability to display interactive content. Prior research therefore identified opportunities for fusing the two mediums into a combined interface. This work presents a literature review on this form of innovation - technologies that bridge the paper-digital gap. First, we synthesize an understanding of paper and its relationship with digital devices through the lens of past works. Then, we outline the state-of-the-art for paper-digital interfaces and highlight possible use cases and implementation approaches. Last, we discuss design considerations and future work for developing paper-digital interfaces. Our work may be beneficial for HCI researchers interested in the development of hybrid paper-digital interfaces, and more broadly in embedding digital functionalities in everyday objects. © 2021 ACM.

The EU-funded FET Proactive Environmental Intelligence project "I-Seed"(Grant Agreement n. 101017940, https://www.iseedproject.eu/) targets towards the development of a radically simplified and environmentally friendly approach for environmental monitoring. Specifically, I-Seed aims at developing a new generation of self-deployable and biodegradable soft miniaturized robots, inspired by the morphology and dispersion abilities of plant seeds, able to perform low-cost, environmentally responsible, in-situ measurements. The natural functional mechanisms of seeds dispersal offer a rich source of robust, highly adaptive, mass and energy efficient mechanisms, and behavioral and morphological intelligence, which can be selected and implemented for advanced, but simple, technological inventions. I-Seed robots are conceived as unique in their movement abilities because inspired by passive mechanisms and materials of natural seeds, and unique in their environmentally friendly design because made of all biodegradable components. Sensing is based on a chemical transduction mechanism in a stimulus-responsive sensor material with fluorescence-based optical readout, which can be read via one or more drones equipped with fluorescent LiDAR technology and a software able to perform a real time georeferencing of data. The I-Seed robotic ecosystem is envisioned to be used for collecting environmental data in-situ with high spatial and temporal resolution across large remote areas where no monitoring data are available, and thus for extending current environmental sensor frameworks and data analysis systems.

IR drop constraint is a fundamental requirement enforced in almost all chip designs. However, its evaluation takes a long time, and mitigation techniques for fixing violations may require numerous iterations. As such, fast and accurate IR drop prediction becomes critical for reducing design turnaround time. Recently, machine learning (ML) techniques have been actively studied for fast IR drop estimation due to their promise and success in many fields. These studies target at various design stages with different emphasis, and accordingly, different ML algorithms are adopted and customized. This paper provides a review to the latest progress in ML-based IR drop estimation techniques. It also serves as a vehicle for discussing some general challenges faced by ML applications in electronics design automation (EDA), and demonstrating how to integrate ML models with conventional techniques for the better efficiency of EDA tools. © 2020 Association on Computer Machinery.

COVID-19 has brought about substantial social, economic and health related burdens, motivating different control measures from policy makers worldwide. Contact tracing plays a pivotal role in the COVID-19 era. However, contact tracing is by nature entirely retrospective: it can only identify contacts of known or suspected cases. Our proposed system is prospective, aiming to 'create' networks that will ultimately make contact tracing and pandemic management easier. As contact tracing seeks to reconstruct the underlying interaction network, we can improve the process by reducing the complexity of contact network structure; we introduce a method for reducing contact network complexity through strategic scheduling. The method functions through pairwise comparison of individual trajectories in a coordinate space of activities, locations, and time intervals. We demonstrate the method through a simulated scenario where individuals (students) register for activities using a mobile application in a campus. The application then applies our algorithm to provide individuals with schedules that reduce the complexity of the overall network, without compromising individual privacy.

Advisory services are ritualized encounters between an expert and an advisee. Empathetic, high-touch relationship between those two parties was identified as the key aspect of a successful advisory encounter. To facilitate the high-touch interaction, advisors established rituals which stress the unique, individual character of each client and each single encounter. Simultaneously, organizations like banks or insurances rolled out tools and technologies for use in advisory services to offer a uniform experience and consistent quality across branches and advisors. As a consequence, advisors were caught between the high-touch and high-tech aspects of an advisory service. This manuscript presents a system that accommodates for high-touch rituals and practices and combines them with high-tech collaboration. The proposed solution augments pen-and-paper practices with digital content and affords new material performances coherent with the existing rituals. The evaluation in realistic mortgage advisory services unveils the potential of mixed reality approaches for application in professional, institutional settings. The blow-by-blow analysis of the conversations reveals how an advisory service can become equally high-tech and high-touch thanks to a careful ritual-oriented system design. As a consequence, this paper presents a solution to the tension between the high-touch and high-tech tendencies in advisory services.

Despite the advent of a flurry of digital technologies, paper prevails on manufacturing shopfloors. To understand the roles and value of paper on the shopfloor, we have studied the manufacturing practices at two state-of-the-art automotive supplier facilities, applying ethnographic fieldwork, in-depth interviews, as well as photo and document analysis. We find that paper has unique affordances that today’s digital technologies cannot easily supplant on current shopfloors. More specifically, we find four paper practices: (1) creating and adapting individual information spaces, (2) reinterpreting information, (3) combining information handover with social interaction, and (4) visual cuing. We discuss these practices and the unique affordance of paper that currently support shopfloor workers and also consider the limitations of paper, which are becoming increasingly apparent, since more tasks increasingly depend on real-time information.

QC 20171201

Augmented Paper (AP) is an important area of Augmented Reality (AR). Many AP systems rely on visual features for paper document identification. Although promising, these systems can hardly support large sets of documents (i.e. one million documents) because of high memory and time cost in handling high-dimensional features. On the other hand, general large-scale image identification techniques are not well customized to AP, costing unnecessarily more resources to achieve the identification accuracy required by AP. To address this mismatching between AP and image identification techniques, we propose a novel large-scale image identification technique well geared to AP. At its core is a geometric verification scheme based on Minimum visual-word Correspondence Set (MICSs). MICS is a set of visual word (i.e. quantized visual feature) correspondences, each of which contains a minimum number of correspondences that are sufficient for deriving a transformation hypothesis between a captured document image and an indexed image. Our method selects appropriate MICSs to vote in a Hough space of transformation parameters, and uses a robust dense region detection algorithm to locate the possible transformation models in the space. The models are then utilized to verify all the visual word correspondences to precisely identify the matching indexed image. By taking advantage of unique geometric constraints in AP, our method can significantly reduce the time and memory cost while achieving high accuracy. As showed in evaluation with two AP systems called FACT and EMM, over a dataset with 1M+ images, our method achieves 100% identification accuracy and 0.67% registration error for FACT; For EMM, our method outperforms the state-of-the-art image identification approach by achieving 4% improvements in detection rate and almost perfect precision, while saving 40% and 70% memory and time cost.

Augmented Paper (AP) is an important area of Augmented Reality (AR). Many AP systems rely on visual features for paper document identification. Although promising, these systems can hardly support large sets of documents (i.e. one million documents) because of high memory and time cost in handling high-dimensional features. On the other hand, general large-scale image identification techniques are not well customized to AP, costing unnecessarily more resources to achieve the identification accuracy required by AP. To address this mismatching between AP and image identification techniques, we propose a novel large-scale image identification technique well geared to AP. At its core is a geometric verification scheme based on Minimum visual-word Correspondence Set (MICSs). MICS is a set of visual word (i.e. quantized visual feature) correspondences, each of which contains a minimum number of correspondences that are sufficient for deriving a transformation hypothesis between a captured document image and an indexed image. Our method selects appropriate MICSs to vote in a Hough space of transformation parameters, and uses a robust dense region detection algorithm to locate the possible transformation models in the space. The models are then utilized to verify all the visual word correspondences to precisely identify the matching indexed image. By taking advantage of unique geometric constraints in AP, our method can significantly reduce the time and memory cost while achieving high accuracy. As showed in evaluation with two AP systems called FACT and EMM, over a dataset with 1M+ images, our method achieves 100% identification accuracy and 0.67% registration error for FACT; For EMM, our method outperforms the state-of-the-art image identification approach by achieving 4% improvements in detection rate and almost perfect precision, while saving 40% and 70% memory and time cost.

Augmented Paper (AP) is an important area of Augmented Reality (AR). Many AP systems rely on visual features for paper document identification. Although promising, these systems can hardly support large sets of documents (i.e. one million documents) because of high memory and time cost in handling high-dimensional features. On the other hand, general large-scale image identification techniques are not well customized to AP, costing unnecessarily more resources to achieve the identification accuracy required by AP. To address this mismatching between AP and image identification techniques, we propose a novel large-scale image identification technique well geared to AP. At its core is a geometric verification scheme based on Minimum visual-word Correspondence Set (MICSs). MICS is a set of visual word (i.e. quantized visual feature) correspondences, each of which contains a minimum number of correspondences that are sufficient for deriving a transformation hypothesis between a captured document image and an indexed image. Our method selects appropriate MICSs to vote in a Hough space of transformation parameters, and uses a robust dense region detection algorithm to locate the possible transformation models in the space. The models are then utilized to verify all the visual word correspondences to precisely identify the matching indexed image. By taking advantage of unique geometric constraints in AP, our method can significantly reduce the time and memory cost while achieving high accuracy. As showed in evaluation with two AP systems called FACT and EMM, over a dataset with 1M+ images, our method achieves 100% identification accuracy and 0.67% registration error for FACT; For EMM, our method outperforms the state-of-the-art image identification approach by achieving 4% improvements in detection rate and almost perfect precision, while saving 40% and 70% memory and time cost.

In this video, we describe a series of techniques for building sensors, actuators and circuit boards that behave, look, and feel like paper. By embedding electro-active inks, conductive threads and smart materials directly into paper during the papermaking process, we have developed seamless composites that are capable of supporting new and unexpected application domains in ubiquitous and pervasive computing at affordable costs.

In this video, we describe a series of techniques for building sensors, actuators and circuit boards that behave, look, and feel like paper. By embedding electro-active inks, conductive threads and smart materials directly into paper during the papermaking process, we have developed seamless composites that are capable of supporting new and unexpected application domains in ubiquitous and pervasive computing at affordable costs.

Advances in the area of ubiquitous computing have paved the way for a plethora of mobile applications operating in smart devices that aim to assist various aspects of everyday life. In the context of human-environment interaction in smart homes, remote monitoring of older adults, or general improvement of daily habits, the estimation of human activities in real-time and in the appropriate context is becoming of increasing importance. At the same time, the ever-increasing capabilities of smartwatches and their premium wrist-attached placement makes them an exciting tool for solving the above problem even for tasks of high manual dexterity. In this work, we propose a hierarchical framework on smartwatch IMU data to learn activities of daily living in varying granularity: from high-level, generic descriptions to low-level detailed activities. We employ a swarm of CNN-LSTM-based classifiers across N hierarchy levels, trained with a set of features tailored to each task addressed. Our proposed hierarchical method achieves tangible improvement in classification accuracy and up to 4x speed-up in inference times compared to the standard non-hierarchical approach. We also contribute a new dataset of smartwatch IMU data used to build and evaluate our method, so that future research in the community can benefit from it.

Natural language processing (NLP) involves the automated analysis and manipulation of human language. This includes algorithms that can analyze text created by humans and algorithms that can generate text that appears natural. Nowadays, NLP methods are becoming increasingly prevalent to enhance various aspects of software development. Indeed, throughout the software development lifecycle, numerous natural language artifacts are produced. Therefore, the existence of NLP-based approaches and tools has opened up possibilities for improving the e ectiveness and productivity of software engineers, processes, and products. The second edition of the Natural Language-Based Software Engineering Workshop (NLBSE) took place in 2023 alongside the 45th International Conference on Software Engineering (ICSE 2023), where the research community engaged in discussions about these approaches. This event brought together researchers and practitioners from the elds of NLP and software engineering to exchange experiences, establish future research directions, and promote the adoption of NLP techniques and tools in tackling challenges specific to software engineering. In this paper, we present a summary of the 2nd edition of the workshop, which comprised three full papers, four short/position papers, five tool competition/demonstration papers, two keynote talks (\Automated Bug Management: Re ections & the Road Ahead" by David Lo and \Trends and Opportunities in the Application of Large Language Models: the Quest for Maximum E ect" by Albert Ziegler), followed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2023.github.io/index. html

2024 will be the largest election year in history involving over 50 countries and approximately 4.2 billion people. Since 1996, the Web has been instrumental in political campaigns, enhancing public engagement and creating new communication avenues for elections. Nevertheless, the proliferation of generative AI technologies has made false information dissemination simpler and quicker, posing a substantial threat to election integrity and democratic processes. The 2024 global elections underscore the need to comprehend and tackle the impact of such technologies on democracy. In this paper, we undertake a detailed meta-analysis, scrutinizing 44 papers published in The Web Conference, detailing the influence of the Web on elections. Our research reveals key historical trends on how the Web has impacted elections: first, social media has revolutionized election strategies through direct voter-candidate interactions. Second, big data and algorithm-driven campaigns are commonplace. Third, AI advancements have exacerbated the spread of fake news, risking election fairness. Predominantly from studies published since 2018 among 44 papers, we underscore the necessity for advanced detection tools, policy formulation, and responsible AI use to maintain electoral integrity. This analysis offers an insight into the Web and AI's impact on elections, presenting pointers for addressing challenges and leveraging opportunities in the 2024 and future elections.

Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks. Questions, however, persist about the nature of LLMs and their potential to integrate common-sense human knowledge when performing tasks involving information about the real physical world. This paper delves into these questions by exploring how LLMs can be extended to interact with and reason about the physical world through IoT sensors and actuators, a concept that we term "Penetrative AI". The paper explores such an extension at two levels of LLMs' ability to penetrate into the physical world via the processing of sensory signals. Our preliminary findings indicate that LLMs, with ChatGPT being the representative example in our exploration, have considerable and unique proficiency in employing the embedded world knowledge for interpreting IoT sensor data and reasoning over them about tasks in the physical realm. Not only this opens up new applications for LLMs beyond traditional text-based tasks, but also enables new ways of incorporating human knowledge in cyber-physical systems.

Nowadays, Machine Learning (ML) systems are widely used in various businesses and are increasingly being adopted to make decisions that can significantly impact people’s lives. However, these decision-making systems rely on data-driven learning, which poses a risk of propagating the bias embedded in the data. Despite various attempts by the algorithmic fairness community to outline different types of bias in data and algorithms, there is still a limited understanding of how these biases relate to the fairness of ML-based decision-making systems. In addition, efforts to mitigate bias and unfairness are often agnostic to the specific type(s) of bias present in the data. This paper explores the nature of fundamental types of bias, discussing their relationship to moral and technical frameworks. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced throughout the entire modelling pipeline and possibly how to mitigate it. Our primary contribution is a framework for generating synthetic datasets with different forms of biases. We use our proposed synthetic data generator to perform experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. Furthermore, we provide initial insights into mitigating specific types of bias through post-processing techniques. The implementation of the synthetic data generator and experiments can be found at https://github.com/rcrupiISP/BiasOnDemand.

Transitioning edge and cloud computing in 5G networks towards service-based architecture increases their complexity as they become even more dynamic and intertwine more actors or delegation levels. In this paper, we demonstrate the Liability-aware security manager Analysis Service (LAS), a framework that uses machine learning techniques to compute liability and trust indicators for service-based architectures such as cloud microservices. Based on the commitments of Service Providers (SPs) and real-time observations collected by a Root Cause Analysis (RCA) tool GRALAF, the LAS computes three categories of liability and trust indicators, specifically, a Commitment Trust Score, Financial Exposure, and Commitment Trends.

Smart eyewear's interaction mode has attracted significant research attention. While most commercial devices have adopted touch panels situated on the temple front of eyeglasses for interaction, this paper identifies a drawback stemming from the unparalleled plane between the touch panel and the display, which disrupts the direct mapping between gestures and the manipulated objects on display. Therefore, this paper proposes RimSense, a proof-of-concept design for smart eyewear, to introduce an alternative realm for interaction - touch gestures on eyewear rim. RimSense leverages piezoelectric (PZT) transducers to convert the eyeglass rim into a touch-sensitive surface. When users touch the rim, the alteration in the eyeglass's structural signal manifests its effect into a channel frequency response (CFR). This allows RimSense to recognize the executed touch gestures based on the collected CFR patterns. Technically, we employ a buffered chirp as the probe signal to fulfil the sensing granularity and noise resistance requirements. Additionally, we present a deep learning-based gesture recognition framework tailored for fine-grained time sequence prediction and further integrated with a Finite-State Machine (FSM) algorithm for event-level prediction to suit the interaction experience for gestures of varying durations. We implement a functional eyewear prototype with two commercial PZT transducers. RimSense can recognize eight touch gestures on the eyeglass rim and estimate gesture durations simultaneously, allowing gestures of varying lengths to serve as distinct inputs. We evaluate the performance of RimSense on 30 subjects and show that it can sense eight gestures and an additional negative class with an F1-score of 0.95 and a relative duration estimation error of 11%. We further make the system work in real-time and conduct a user study on 14 subjects to assess the practicability of RimSense through interactions with two demo applications. The user study demonstrates

Graph processing system design has been widely considered to be a challenging topic due to the mismatch between the computational throughput requirement and the memory bandwidth. Recent works try to deliver better graph processing systems by taking advantage of application-specific architectures and emerging high-bandwidth memory on FPGAs. However, there is still ample room for improvements regarding flexibility, scalability, and usability. This paper presents GraFlex, a flexible scatter-gather graph processing framework on FPGAs with scalable interconnection networks. It adopts the Bulk-Synchronous Parallel (BSP) paradigm for global control and synchronization, enabling rapid deployment of performant graph processing systems through HLS-based design flows. GraFlex conducts software-hardware co-optimization to boost system performance. It configures the compact graph format, partition scheme, and memory channel allocation strategy to support scalable designs. Resource-efficient multi-stage butterfly interconnection network achieves on-device data communication and facilitates throughput matching. To handle fragmented memory requests, we propose coalesced memory access engines to improve bandwidth utilization. GraFlex is comprehensively evaluated with various graph applications and real-world datasets. Our results show up to 2.09\texttimes average speedup in traversal throughput over the existing state-of-the-art work with a non-negligible reduction in power and resource consumption. A case study of the breadth-first search (BFS) application shows a 6.58\texttimes speedup in average algorithm throughout with proper implementation choices enabled by the scatter-gather mechanism implemented. The BFS study also reports an almost linear throughput scaling versus the number of processing elements (PEs) and memory channels.

Sparse operators, i.e., operators that take sparse tensors as input, are of great importance in deep learning models. Due to the diverse sparsity patterns in different sparse tensors, it is challenging to optimize sparse operators by seeking an optimal sparse format, i.e., leading to the lowest operator latency. Existing works propose to decompose a sparse tensor into several parts and search for a hybrid of sparse formats to handle diverse sparse patterns. However, they often make a trade-off between search space and search time: their search spaces are limited in some cases, resulting in limited operator running efficiency they can achieve. In this paper, we try to extend the search space in its breadth (by doing flexible sparse tensor transformations) and depth (by enabling multi-level decomposition). We formally define the multi-level sparse format decomposition problem, which is NP-hard, and we propose a framework STile for it. To search efficiently, a greedy algorithm is used, which is guided by a cost model about the latency of computing a sub-task of the original operator after decomposing the sparse tensor. Experiments of two common kinds of sparse operators, SpMM and SDDMM, are conducted on various sparsity patterns, and we achieve 2.1-18.0× speedup against cuSPARSE on SpMMs and 1.5 - 6.9× speedup against DGL on SDDMM. The search time is less than one hour for any tested sparse operator, which can be amortized.

The similarity matrix is at the core of similarity search problems. However, incomplete observations are ubiquitous in real scenarios leading to a less accurate similarity matrix. To alleviate this problem, in this paper, based on the key insight that the similarity matrix enjoys both the symmetric and positive semi-definiteness (PSD) properties, we propose a novel similarity matrix calibration method, which is scalable, effective, and sound. Specifically, we establish the PSD property as a constraint for the similarity matrix calibration problem and propose a novel similarity matrix calibration method to estimate the similarity matrix, which approximates the unknown complete ground-truth similarity matrix. To enable a fast optimization process, we further develop a general approximated algorithm that bypasses the computation of singular values. Theoretical analysis ensures stable calibration performance and convergence speed. Extensive experiments of similarity matrix calibration on real-world datasets demonstrate that our proposed method outperforms baseline methods in terms of both accuracy and speed.

Recent advances in large language models (LLMs) significantly boost their usage in software engineering. However, training a well-performing LLM demands a substantial workforce for data collection and annotation. Moreover, training datasets may be proprietary or partially open, and the process often requires a costly GPU cluster. The intellectual property value of commercial LLMs makes them attractive targets for imitation attacks, but creating an imitation model with comparable parameters still incurs high costs. This motivates us to explore a practical and novel direction: slicing commercial black-box LLMs using medium-sized backbone models. In this paper, we explore the feasibility of launching imitation attacks on LLMs to extract their specialized code abilities, such as

Data storytelling is powerful for communicating data insights, but it requires diverse skills and considerable effort from human creators. Recent research has widely explored the potential for artificial intelligence (AI) to support and augment humans in data storytelling. However, there lacks a systematic review to understand data storytelling tools from the perspective of human-AI collaboration, which hinders researchers from reflecting on the existing collaborative tool designs that promote humans’ and AI's advantages and mitigate their shortcomings. This paper investigated existing tools with a framework from two perspectives: the stages in the storytelling workflow where a tool serves, including analysis, planning, implementation, and communication, and the roles of humans and AI in each stage, such as creators, assistants, optimizers, and reviewers. Through our analysis, we recognize the common collaboration patterns in existing tools, summarize lessons learned from these patterns, and further illustrate research opportunities for human-AI collaboration in data storytelling.

In AI-assisted decision-making, it is crucial but challenging for humans to achieve appropriate reliance on AI. This paper approaches this problem from a human-centered perspective, “human self-confidence calibration”. We begin by proposing an analytical framework to highlight the importance of calibrated human self-confidence. In our first study, we explore the relationship between human self-confidence appropriateness and reliance appropriateness. Then in our second study, We propose three calibration mechanisms and compare their effects on humans’ self-confidence and user experience. Subsequently, our third study investigates the effects of self-confidence calibration on AI-assisted decision-making. Results show that calibrating human self-confidence enhances human-AI team performance and encourages more rational reliance on AI (in some aspects) compared to uncalibrated baselines. Finally, we discuss our main findings and provide implications for designing future AI-assisted decision-making interfaces.

This paper presents Siro, a new program transformation framework that translates between different versions of Intermediate Representations (IR), aiming to better address the issue of IR version incompatibility on IR-based software, such as static analyzers. We introduce a generic algorithm skeleton for Siro based on the divide-and-conquer principle. To minimize labor-intensive tasks of the implementation process, we further employ program synthesis to automatically generate translators for IR instructions within vast search spaces. Siro is instantiated on LLVM IR and has effectively helped to produce ten well-functioning IR translators for different version pairs, each taking less than three hours. From a practical perspective, we utilize these translators to assist static analyzers and fuzzers in reporting bugs and achieving accuracy of 91% and 95%, respectively. Remarkably, Siro has already been deployed in real-world scenarios and makes existing static analyzers available to safeguard the Linux kernel by uncovering 80 new vulnerabilities.

Large language models (LLMs) have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience with the objective of advancing research and applications in this field. To this end, we present the first-ever LLM in geoscience, K2, alongside a suite of resources developed to further promote LLM research within geoscience. For instance, we have curated the first geoscience instruction tuning dataset, GeoSignal, which aims to align LLM responses to geoscience-related user queries. Additionally, we have established the first geoscience benchmark, GeoBench, to evaluate LLMs in the context of geoscience. In this work, we experiment with a complete recipe to adapt a pre-trained general-domain LLM to the geoscience domain. Specifically, we further train the LLaMA-7B model on 5.5B tokens of geoscience text corpus, including over 1 million pieces of geoscience literature, and utilize GeoSignal's supervised data to fine-tune the model. Moreover, we share a protocol that can efficiently gather domain-specific data and construct domain-supervised data, even in situations where manpower is scarce. Meanwhile, we equip K2 with the abilities of using tools to be a naive geoscience aide. Experiments conducted on the GeoBench demonstrate the effectiveness of our approach and datasets on geoscience knowledge understanding and utilization.We open-source all the training data and K2 model checkpoints at https://github.com/davendw49/k2

Migration and aging-related dilemmas have limited the opportunities for late-life migrants to rebuild social connections and access support. While research on migrants has drawn increasing attention in HCI, limited attention has been paid to the increasing number of late-life migrants. This paper reports a qualitative study examining the social connections and support of late-life migrants. In particular, drawing on the social convoy model, we pay specific attention to the dynamic changes of late-life migrants’ social convoy, the supporting roles each convoy plays, the functions ICT plays in the process, as well as the encountered challenges and expectations of late-life migrants regarding ICT-supported social convoys. Based on these findings, we deeply discuss the role of the social convoy in supporting more targeted social support for late-life migrants, as well as broader migrant communities. Finally, we offer late-life migrant-oriented design considerations.

Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Causal discovery extensively involves conditional independence (CI) tests. Hence, its output quality highly depends on the performance of CI tests, which can often be unreliable in practice. Moreover, privacy concerns arise when excessive CI tests are performed. Despite the distinct nature between unreliable and excessive CI tests, this paper identifies a unified and principled approach to addressing both of them. Generally, CI statements, the outputs of CI tests, adhere to Pearl's axioms, which are a set of well-established integrity constraints on conditional independence. Hence, we can either detect erroneous CI statements if they violate Pearl's axioms or prune excessive CI statements if they are logically entailed by Pearl's axioms. Holistically, both problems boil down to reasoning about the consistency of CI statements under Pearl's axioms (referred to as CIR problem). We propose a runtime verification tool called CICheck, designed to harden causal discovery algorithms from reliability and privacy perspectives. CICheck employs a sound and decidable encoding scheme that translates CIR into SMT problems. To solve the CIR problem efficiently, CICheck introduces a four-stage decision procedure with three lightweight optimizations that actively prove or refute consistency, and only resort to costly SMT-based reasoning when necessary. Based on the decision procedure to CIR, CICheck includes two variants: ED-Check and P-Check, which detect erroneous CI tests (to enhance reliability) and prune excessive CI tests (to enhance privacy), respectively. We evaluate CICheck on four real-world datasets and 100 CIR instances, showing its effectiveness in detecting erroneous CI tests and reducing excessive CI tests while retaining practical performance.

In a recent paper [1], Brian Hedden has argued that most of the group fairness constraints discussed in the machine learning literature are not necessary conditions for the fairness of predictions, and hence that there are no genuine fairness metrics. This is proven by discussing a special case of a fair prediction. In our paper, we show that Hedden's argument does not hold for the most common kind of predictions used in data science, which are about people and based on data from similar people; we call these “human-group-based practices.” We argue that there is a morally salient distinction between human-group-based practices and those that are based on data of only one person, which we call “human-individual-based practices.” Thus, what may be a necessary condition for the fairness of human-group-based practices may not be a necessary condition for the fairness of human-individual-based practices, on which Hedden's argument is based. Accordingly, the group fairness metrics discussed in the machine learning literature may still be relevant for most applications of prediction-based decision making.

This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather systems, whose variables are multi-modal with scalars, vectors, and functions. This paper takes the traffic congestion analysis as a concrete case, where a traffic intersection is usually regarded as a DAG. In a road network of multiple intersections, different intersections can only have someoverlapping and distinct variables observed. For example, a signalized intersection has traffic light-related variables, whereas unsignalized ones do not. This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized. To this end, we innovatively propose a multi-modal regression for linear causal relationship description of different variables. Then we develop a novel Causality Difference (CD) measure and its differentiable approximator. Compared with existing SOTA measures, CD can penalize the causal structural difference among DAGs with distinct nodes and can better consider the uncertainty of causal orders. We rigidly prove our design's topological interpretation and consistency properties. We conduct thorough simulations and one case study to show the effectiveness of our MM-DAG. The code is available under https://github.com/Lantian72/MM-DAG. © 2023 Owner/Author.

Recent awareness of privacy protection and compliance requirement resulted in a controversial view of recommendation system due to personal data usage. Therefore, privacy-protected recommendation emerges as a novel research direction. In this paper, we first formulate this problem as a vertical federated learning problem, i.e., features are vertically distributed over different departments. We study a contextual bandit learning problem for recommendation in the vertical federated setting. To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask mechanism (O3M). O3M mechanism, a tailored component for contextual bandits by carefully exploiting their shared structure, can ensure privacy protection while avoiding expensive conventional cryptographic techniques. We further apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instantiate two practical protocols for online recommendation. The proposed protocols can perfectly recover the service quality of centralized bandit algorithms while achieving a satisfactory runtime efficiency, which is theoretically proved and analysed in this paper. By conducting extensive experiments on both synthetic and real-world datasets, we show the superiority of the proposed method in terms of privacy protection and recommendation performance. © 2023 ACM.

Graph self-supervised learning aims to mine useful information from unlabeled graph data, and has been successfully applied to pre-train graph representations. Many existing approaches use contrastive learning to learn powerful embeddings by learning contrastively from two augmented graph views. However, none of these graph contrastive methods fully exploits the diversity of different augmentations, and hence is prone to overfitting and limited generalization ability of learned representations. In this paper, we propose a novel Graph Self-supervised Learning method with Augmentation-aware Contrastive Learning. Our method is based on the finding that the pre-trained model after adding augmentation diversity can achieve better generalization ability. To make full use of the information from the diverse augmentation method, this paper constructs new augmentation-aware prediction task which complementary with the contrastive learning task. Similar to how pre-training requires fast adaptation to different downstream tasks, we simulate train-test adaptation on the constructed tasks for further enhancing the learning ability; this strategy can be deemed as a form of meta-learning. Experimental results show that our method outperforms previous methods and learns better representations for a variety of downstream tasks. © 2023 ACM.

With the advancement of virtual reality (VR) technology, virtual displays have become integral to how museums, galleries, and other tourist destinations present their collections to the public. However, the current lack of immersion in virtual reality displays limits the user's ability to experience and appreciate its aesthetics. This paper presents a case study of a creative approach taken by a tourist attraction venue in developing a physical network system that allows visitors to enhance VR's aesthetic aspects based on environmental parameters gathered by external sensors. Our system was collaboratively developed through interviews and sessions with twelve stakeholder groups interested in art and exhibitions. This paper demonstrates how our technological advancements in interaction, immersion and visual attractiveness surpass those of earlier virtual display generations. Through multimodal interaction, we aim to encourage innovation on the Web and create more visually appealing and engaging virtual displays. It is hoped that the greater online art community will gain fresh insight into how people interact with virtual worlds as a result of this work. © 2023 ACM.

In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the machine learning literature: 1) calibration between groups and 2) equality of false positive and false negative rates between groups. In our paper, we focus on moral arguments in support of either measure. The conflict between group calibration vs. false positive and false negative rate equality is one of the core issues in the debate about group fairness definitions among practitioners. For any thorough moral analysis, the meaning of the term “fairness” has to be made explicit and defined properly. For our paper, we equate fairness with (non-)discrimination, which is a legitimate understanding in the discussion about group fairness. More specifically, we equate it with “prima facie wrongful discrimination” in the sense this is used in Prof. Lippert-Rasmussen's treatment of this definition. In this paper, we argue that a violation of group calibration may be unfair in some cases, but not unfair in others. Our argument analyzes in great detail two specific hypothetical examples of usage of predictions in decision making. The most important practical implication is that between-group calibration is defensible as a bias standard in some cases but not others; we show this by referring to examples in which the violation of between-group calibration is discriminatory, and others in which it is not. This is in line with claims already advanced in the literature, that algorithmic fairness should be defined in a way that is sensitive to context. The most important practical implication is that arguments based on examples in which fairness requires between-group calibration, or equality in the false-positive/false-negative rates, do no generalize. For it may be that group calibration is a fairness requirement in one case, but not in another.

Natural language processing (NLP) refers to automatic computational processing of human language, including both algorithms that take human-produced text as input and algorithms that produce natural-looking text as outputs. There is a widespread and growing usage of NLP approaches to optimize many aspects of the development process of software systems. In particular, since natural language artifacts are used and reused during the software development lifecycle, the availability of natural language-based approaches and tools enabled the envisioning of methods for improving efficiency in software engineers, processes, and products. The research community has been discussing these approaches in the 1st edition of the Natural Language-Based Software Engineering Workshop (NLBSE), collocated with ICSE (the International Conference on Software Engineering) in 2022. This event brought together researchers and industrial practitioners from NLP and the software engineering community to share experiences, provide directions for future research, and encourage the usage of NLP techniques and tools for addressing software engineeringspecific challenges. In this paper, we present a summary of the 1st edition of the workshop, which comprised five full papers, four short/position papers, five tool competition/demonstration papers, one keynote (“Deep Learning & Software Engineering: Past, Present and Future” by Denys Poshyvanyk), followed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2022.github.io/index.html

This paper explores the concept of expanded cinema and its relationship to extended reality (XR), focusing on the potential of artificial intelligence (AI) to expand and extend expressive possibilities. Expanded cinema refers to experimental film and multimedia art forms that challenge the conventions of traditional cinema by creating immersive and interactive experiences for audiences. XR, on the other hand, blurs the line between physical and virtual reality, offering immersive storytelling experiences. Both expanded cinema and XR aim to push the boundaries of traditional norms and create immersive experiences through the integration of technology, interactivity, and cross-sensory elements. The paper emphasizes the role of AI in optimizing 3D scene creation for XR and enhancing the overall experience through a case study. It also presents several AI-based techniques, such as generative models and AI-assisted rendering, that facilitate efficient and effective 3D content creation. Additionally, it explores the use of AI plugins in 3D modeling software and the generation of 3D models and textures from 2D images using techniques like GANs and VAEs. The incorporation of AI to extend and expand opens up new possibilities for immersive experiences in the future. © 2023 ACM.

Virtual reality (VR) and associated technologies have rapidly grew, creating new opportunities for improving mental health. In order to provide an immersive and concentrated meditation experience, this paper offers the idea of VR-assisted meditation, which integrates the advantages of VR technology with mindfulness techniques. The suggested technique, which is known as LOOP Meditation, is mainly aimed toward novice meditators and places a strong emphasis on the value of movement and breath awareness when meditating. Existing VR experiences and meditation applications sometimes ignore the value of including physical movement, which can improve mindful body sensations and maintain interest. By creating a software that incorporates body movement and breath sensing into virtual reality surroundings, LOOP Meditation addresses this gap. The LOOP Meditation design and implementation are examined in this paper along with its possible advantages and consequences for people looking to develop their meditation practice. A pilot research comparing the efficacy of this strategy to conventional meditation techniques is offered. The study's findings add to the expanding body of knowledge on VR-assisted meditation and demonstrate how it may have a positive effect on mental health. © 2023 ACM.

In Switzerland, SMEs account for 99% of the market and employ around two-thirds of all employees. Many of these SMEs already use BI systems and generate business and operational data. Nevertheless, in many cases the potential that could arise from the data is not exploited. As a result, SMEs are falling behind large companies, as empirical studies show. From this, the problem can be derived that despite existing data in Swiss SMEs, the benefit in terms of Business Intelligence (BI) is unrecognized. The goal of this paper is to develop a framework model for the introduction of Business Intelligence in SMEs. The model should be structured generically and show on a meta-level which points are important for the introduction of BI and what the procedure for this is. In this paper we work within a research framework for information systems, using a design science research approach and a method engineering approach for the development of the model. In the development of the model, findings from a previous work were included as well as other studies dealing with the implementation of business intelligence. For the evaluation of the developed model, SMEs as well as business intelligence consultants were interviewed. The answers to the scope of the model were positive. However, there were suggestions to improve the model. Especially for SMEs it is important that business cases are discussed and solutions and their benefits are clearly shown.